Learning to Cooperate in Decentralized Wireless Networks

By Minhoe Kim, Paul de Kerret, David Gesbert. 2018

16 citations recorded by the source corpus.

Several key wireless communication setups call for coordination capabilities between otherwise interfering transmitters. Coordination or cooperation can be achieved at the expense of channel state information exchange. When such information is noisy, the derivation of robust decision-making algorithms is unfortunately known to be very challenging via conventional optimization method. In this paper we introduce a learning-based framework which allows the agents, aka. the transmitters, to produce as-relevant-as-possible messages to each other on the basis of arbitrarily partial and noisy local channel state information. The messages are produced via distributed deep neural networks (DNNs) which are trained for a specific coordination purpose. The message-passing DNNs are completed with decision-making DNNs which are trained for a network metric maximization. Promising preliminary results are obtained in the context of sum-rate maximizing decentralized power control.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Deep Learning for Distributed Optimization: Applications to Wireless Resource Management (2019, methodology): “individual transmitters equipped with their own DNN for the distributed computation in [9], [10], [21], [22].”
Machine Learning in the Air (2019, background): “This approach is extended in [78] to the design via DNN of instantaneous and quantized message exchanges between the transmitters, where it is highlighted how joint optimization of message sharing and transmission provide more robustness.”